The aforementioned DNF formulae can be generalized to rule models, which have received
a great deal of attention in the literature of machine learning and knowledge discovery (see e.g. [1]}, [2]} for general surveys).
Notably, decision lists [3]} are ordered multi-sets of rules of the form
\(L = \langle t_1, c_1\rangle , \ldots , \langle t_m, c_m\rangle \) , where each \(t_i\)  (\(i \in [m]\) ) is a term over \(X_n\) ,
and each \(c_i\)  is a Boolean value in \(\mathbb {B}\) .
An input instance \(\mathbf {x} \in \mathbb {B}^n\)  is a model of \(L\)  if the class \(c_i\)  of the first rule \(t_i\)  that is matched on \(\mathbf {x}\)  is positive.
By convention, the last rule \(t_m\)  is the empty term \(\top \) . Formally, \(L(\mathbf {x}) = c_j\)  where \(j = \mathrm {argmin}_{i = 1}^m \lbrace t_i(\mathbf {x}) = 1\rbrace \) .
The size of a decision list \(L\)  is the sum of the sizes
of the terms occurring in \(L\) .
